Metrics workshop
Align definitions, owners, and source systems so “revenue” means one thing before anyone builds charts.
ADD AI
Data Analytics
Data warehouses, lakehouses, and analytics products that turn operational data into decisions.
Overview
How we approach data analytics.
Dashboards multiply while trust collapses when metric definitions disagree across teams. We start with a semantic layer and owned definitions — then build pipelines and products operators will actually open on Monday morning.
Warehouse or lakehouse choices follow the questions the business must answer. Freshness SLAs, quality checks, and governance that doesn’t freeze shipping are part of the delivery, not a separate “data office” theater.
The point
Numbers the business can act on — without waiting a week for a one-off extract.
Deliverables
Concrete metrics layers, not just dashboards.
Every engagement leaves operators with trusted numbers, pipelines they can trust, and governance that does not freeze the company.
- Metrics definitions and trusted semantic layer
- Warehouse or lakehouse architecture decision records
- Pipelines from operational systems to decision-ready tables
- Dashboards and analytics products operators will open
- Data quality checks and freshness SLAs
- Governance notes, access model, and handover docs
Engagement Model
Define metrics, stand up the warehouse path, ship analytics products, or keep pipeline care.
Pipeline foundation
Warehouse/lakehouse path, core marts, and quality checks with freshness SLAs leadership can trust.
Analytics products
Dashboards and self-serve models for the decisions operators make weekly — not vanity walls of charts.
Data platform care
Ongoing pipeline ownership, incident response for broken loads, and governed expansion of metrics.
Timeline
From metric definitions to trusted tables and operator dashboards.
Week 1
Definitions and source map
Workshop critical metrics, owners, and source systems. Document disagreements before modeling them into stone.
Weeks 2–4
Warehouse path and core marts
Stand up ingestion, transform, and a small set of decision-ready tables with quality tests.
Weeks 5–7
Dashboards and access model
Ship analytics products, enforce access, and validate freshness against real operator schedules.
Handover
Governance and ownership
Runbooks for failed loads, metric change process, and clear owners so the layer doesn’t rot.
What we need from you
Owners and source access that keep metrics honest.
Business owners for definitions beat IT-only projects that ship unused charts.
- Named owners for each critical metric definition
- Read access to operational databases, SaaS exports, and event streams
- Warehouse or cloud account where pipelines will run
- List of decisions and audiences that must use the numbers
- Current pain: conflicting reports, stale extracts, or audit pressure
Common mistakes we help avoid
Analytics mistakes that create spreadsheet shadow IT.
We design the layer so these don’t become the default again next quarter.
- Building dashboards before agreeing metric definitions
- No freshness or quality checks — numbers look fine until they’re wrong
- Governance so heavy that teams abandon the warehouse for CSVs
- One giant “everything” model nobody can understand or change
- Ignoring access and PII until legal blocks the launch
FAQs
Answers before we get on a call.
Do you prefer a specific warehouse vendor?+
We fit the stack to your cloud and skills — BigQuery, Snowflake, Redshift, and similar. The metric layer matters more than the logo.
Can you work with our existing BI tool?+
Yes. We often keep Looker, Power BI, or Metabase and fix the underlying marts and definitions first.
How do you handle conflicting definitions?+
Workshops produce written owners and decision records. Disagreements get resolved or explicitly versioned — not hidden in SQL.
What does handover look like?+
Pipeline docs, quality alerts, metric change process, and named owners so your team can extend without us.